Unravelling Hot Spots: a comprehensive computational mutagenesis study


As protein–protein interactions are critical for all biological functions, representing a large and important class of targets for human therapeutics, identification of protein–protein interaction sites and detection of specific amino acid residues that contribute to the specificity and strength of protein interactions is very important in the biochemistry field. Alanine scanning mutagenesis has allowed the discovery of energetically crucial determinants for protein association that have been defined as hot spots. Systematic experimental mutagenesis is very laborious and time-consuming to perform, and thus it is important to achieve an accurate, predictive computational methodology for alanine scanning mutagenesis, capable of reproducing the experimental mutagenesis values. Having as a basis the MM–PBSA approach first developed by Massova et al., we performed a complete study of the influence of the variation of different parameters, such as the internal dielectric constant, the solvent representation, and the number of trajectories, in the accuracy of the free energy binding differences. As a result, we present here a very simple and fast methodological approach that achieved an overall success rate of 82% in reproducing the experimental mutagenesis data.

This is a preview of subscription content, log in to check access.


  1. 1.

    Arkin MR, Wells AJ (2004). Drug Discov 3:301–317

    CAS  Article  Google Scholar 

  2. 2.

    Sharma SK, Ramsey TM, Bair KW (2002). Curr Med Chem Anticancer Agents 2:311–330

    CAS  Article  Google Scholar 

  3. 3.

    Bogan AA, Thorn KS (1998). J Mol Biol 280:1–9

    CAS  Article  Google Scholar 

  4. 4.

    Delano WL, Ultsch MH, de Vos AM, Wells JA (2000). Science 287:1279–1283

    CAS  Article  Google Scholar 

  5. 5.

    Pons J, Rajpal A, Kirsch J (1999). Protein Sci 8:958–968

    CAS  Google Scholar 

  6. 6.

    Keskin O, Ma B, Nussinov R (2005). J Mol Biol 345:1281–1294

    CAS  Article  Google Scholar 

  7. 7.

    Arkin MR, Randal M, DeLano WL, Hyde J, Luong TN, Oslob JD, Raphael DR, Taylor L, Wang J, McDowell RS, Wells JA, Braisted A (2003). Proc Natl Acad Sci USA 100:1603–1608

    CAS  Article  Google Scholar 

  8. 8.

    Gao Y, Wang R, Lia L (2004). J Mol Model 10:44–54

    CAS  Article  Google Scholar 

  9. 9.

    Lopez MA, Kollman PA (1993). Protein Sci 2:1975–1986

    CAS  Article  Google Scholar 

  10. 10.

    Kortemme T, Baker D (2002). Proc Natl Acad Sci USA 99:14116–14121

    CAS  Article  Google Scholar 

  11. 11.

    Kortemme T, Kim DE, Baker D (2004). Sci STKE 219:12–15

    Google Scholar 

  12. 12.

    Schapira M, Totrov M, Abagyan RJ (1999). Mol Recognit 12:177–190

    CAS  Article  Google Scholar 

  13. 13.

    Aqvist J, Medina C, Samuelsson JE (1994). Protein Eng 7:385–391

    CAS  Google Scholar 

  14. 14.

    Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Freer ST, Rose PM (2002). Proteins 48:539–557

    CAS  Article  Google Scholar 

  15. 15.

    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE III (2000). Acc Chem Res 33:889–897

    CAS  Article  Google Scholar 

  16. 16.

    Wang W, Donini O, Reyes CM, Kollman PA (2002). Annu Rev Biophys Biomol Struct 30:211–243

    Article  Google Scholar 

  17. 17.

    Massova I, Kollman PA (1999). J Am Chem Soc 121:8133–8143

    CAS  Article  Google Scholar 

  18. 18.

    Wang J, Morin P, Wang W, Kollman PA (2001). J Am Chem Soc 123:5221–5230

    CAS  Article  Google Scholar 

  19. 19.

    Wang W, Kollman PA (2002). J Mol Biol 303:567–582

    Article  Google Scholar 

  20. 20.

    Reyes CM, Kollman PA (2000). J Mol Biol 295:1–6

    CAS  Article  Google Scholar 

  21. 21.

    Huo S, Massova I, Kollman PA (2002). J Comput Chem 23:15–27

    CAS  Article  Google Scholar 

  22. 22.

    Mosyak L, Zhang Y, Glasfeld E, Haney S, Stahl M, Seehra J, Somers WS (2000). EMBO J 19:3179–3191

    CAS  Article  Google Scholar 

  23. 23.

    Sauer-Eriksson AE, Kleywegt GJ, Uhlen M, Jones TA (1995). Structure 3:265–278

    CAS  Article  Google Scholar 

  24. 24.

    Bhat TN, Bentley GA, Boulot G, Greene MI, Tello D, Dall’AcquaW, Souchon H, Schwarz FP, Mariuzza RA, Poljak RJ (1994). Proc Natl Acad Sci USA 9:1089–1093

    Article  Google Scholar 

  25. 25.

    Case DA, Darden TA, Cheatham TE III, Simmerling CL, Wang J, Duke RE, Luo R, Merz KM, Wang B, Pearlman DA, Crowley M, Brozell S, Tsui V, Gohlke H, Mongan J, Hornak V, Cui G, Beroza P, Schafmeister C, Caldwell JW, Ross WS, Kollman PA (2004). AMBER 8 University of California, San Francisco

  26. 26.

    Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995). J Am Chem Soc 117:5179–5197

    CAS  Article  Google Scholar 

  27. 27.

    Jorgensen WL, Chandrasekhar J, Madura J, Impey RW, Klein ML (1983). J Chem Phys 79:926–935

    CAS  Article  Google Scholar 

  28. 28.

    Ryckaert JP, Ciccotti G, Berendsen HJ (1977). J Comput Phys 23:327–335

    CAS  Article  Google Scholar 

  29. 29.

    Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (1984). J Chem Phys 81:3684–3690

    CAS  Article  Google Scholar 

  30. 30.

    Case DA, Pearlman DA, Caldwell JW, Cheatham III TE, Ross WS, Simmerling CL, Darden TA, Merz KM, Stanton RV, Cheng AL, Vincent JJ, Crowley M, Tsui V, Radmer R J, Duan Y, Pitera J, Massova I, Seibel GL, Singh UC, Weiner PK, Kollman PA (1999). AMBER 6 University of California, San Francisco

  31. 31.

    Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG (1995). J Chem Phys 103:8577–8593

    CAS  Article  Google Scholar 

  32. 32.

    Pastor RW, Brooks BR, Szabo A (1988). Mol Phys 65:1409–1419

    Article  Google Scholar 

  33. 33.

    Loncharich RJ, Brooks BR, Pastor RW (1992). Biopolymers 32:523–535

    CAS  Article  Google Scholar 

  34. 34.

    Izaguirre JA, Catarello DP, Wozniak JM, Skeel RD (2001). J Chem Phys 114:2090–2098

    CAS  Article  Google Scholar 

  35. 35.

    Tsui V, Case DA (2001). Biopolymers (Nucl Acid Sci). 56:275–291

    CAS  Article  Google Scholar 

  36. 36.

    Rocchia W, Sridharan S, Nicholls A, Alexov E, Chiabrera A, Honig B (2002). J Comput Chem 23:128–137

    CAS  Article  Google Scholar 

  37. 37.

    Rocchia W, Alexov E, Honig B (2001). J Phys Chem B 105:6507–6514

    CAS  Article  Google Scholar 

  38. 38.

    Sitkoff D, Sharp KA, Honig BJ (1994). Phys Chem 98:1978

    CAS  Article  Google Scholar 

  39. 39.

    Moreira IS, Fernandes PA, Ramos MJ (2005). J Mol Struct (Theochem). 729:11–18

    CAS  Article  Google Scholar 

  40. 40.

    Connolly ML (1983). J Appl Cryst 16:548–558

    CAS  Article  Google Scholar 

  41. 41.

    Gao Y, Wang R, Lia L (2004). J Mol Model 10:44–54

    CAS  Article  Google Scholar 

  42. 42.

    Xia B, Tsui V, Case DA, Dyson J, Wright PE (2002). J Biomol NMR 22:317–331

    CAS  Article  Google Scholar 

  43. 43.

    Sheinerman FB, Norel R, Honig B (2000). Curr Opin Struct Biol 10:153–159

    CAS  Article  Google Scholar 

  44. 44.

    Schutz CN, Warshel A (2001). Proteins 44:400–417

    CAS  Article  Google Scholar 

  45. 45.

    Hsieh MJ, Luo R (2004). Proteins 56:475–486

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Maria J. Ramos.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Moreira, I.S., Fernandes, P.A. & Ramos, M.J. Unravelling Hot Spots: a comprehensive computational mutagenesis study. Theor Chem Acc 117, 99–113 (2007). https://doi.org/10.1007/s00214-006-0151-z

Download citation


  • Molecular dynamics
  • Alanine scanning
  • mutagenesis
  • Mutagenesis
  • Free binding energy
  • Hot spots